Profiles of Resilience and Risk in Chronic Pain: Loneliness, Mindfulness, and Optimism Coming Out of the Pandemic

Abstract

Individuals experience variable degrees of severity and interference from chronic pain. While clinical diagnosis and pain symptomology may importantly inform treatment options, person-level characteristics may also impact treatment efficacy. The biopsychosocial model of pain includes several pain-modulatory factors which may inform meaningful categorization, or clustering, of individuals with chronic pain, and help predict pain outcomes. In this observational, longitudinal study conducted approximately shortly after the onset of social distancing, patients with chronic pain (N=94) completed validated assessments of known psychosocial (depression, stress, sleep, catastrophizing) pain modulators and pain intensity, which were used to empirically cluster patients into 3 groups using a two-step hierarchical approach. Subsequently, at 1 year later, degree of pain interference, loneliness, social support, mindfulness, and optimism were compared between these 3 groups using ANOVAs. Participants clustered empirically into three groups: 1) Global Elevation Symptoms (GES), characterized by high psychological distress and moderate pain intensity; 2) Pain Intensity Predominant (PIP), characterized by high pain intensity, but average psychological distress; and 3) Less Elevated Symptoms (LES), characterized by low pain intensity and psychological distress. At the 1-year follow-up, patients in the GES cluster reported significantly greater pain interference than the other two clusters, as well as greater feelings of loneliness and lower mindfulness and optimism compared to the LES cluster. This study supports prior research suggesting that psychosocial-based clustering of patients can be used to identify distinct groups of chronic pain patients. In particular, patients identified as belonging to the GES cluster (high psychological distress, high pain intensity early after onset of social distancing) were at greater risk of suffering from pain a year later, as well as loneliness and less mindfulness and optimism. Patient clustering techniques may help identify high risk patients and suggest behavioral interventions to improve pain by addressing psychosocial modulators of pain. Grant support from 5R35GM128691-02.